OpenRaiser/Intern-Atlas huggingface dataset:
Jointly Optimizing Power Allocation and Device Association for Robust IoT Networks under Infeasible Circumstances
Venue: arXiv cs.IT 2024
Year: 2024

Jointly optimizing power allocation and device association is crucial in Internet-of-Things (IoT) networks to ensure devices achieve their data throughput requirements. Device association, which assigns IoT devices to specific access points (APs), critically impacts resource allocation. Many existing works often assume all data throughput requirements are satisfied, which is impractical given resource limitations and diverse demands. When requirements cannot be met, the system becomes infeasible, causing congestion and degraded performance. To address this problem, we propose a novel framework to enhance IoT system robustness by solving two problems, comprising maximizing the number of satisfied IoT devices and jointly maximizing both the number of satisfied devices and total network throughput. These objectives often conflict under infeasible circumstances, necessitating a careful balance. We thus propose a modified branch-and-bound (BB)-based method to solve the first problem. An iterative algorithm is proposed for the second problem that gradually increases the number of satisfied IoT devices and improves the total network throughput. We employ a logarithmic approximation for a lower bound on data throughput and design a fixed-point algorithm for power allocation, followed by a coalition game-based method for device association. Numerical results demonstrate the efficiency of the proposed algorithm, serving fewer devices than the BB-based method but with faster running time and higher total throughput.
AI and the Future of Work in Africa White Paper
Venue: arXiv cs.HC 2024
Year: 2024

This white paper is the output of a multidisciplinary workshop in Nairobi (Nov 2023). Led by a cross-organisational team including Microsoft Research, NEPAD, Lelapa AI, and University of Oxford. The workshop brought together diverse thought-leaders from various sectors and backgrounds to discuss the implications of Generative AI for the future of work in Africa. Discussions centred around four key themes: Macroeconomic Impacts; Jobs, Skills and Labour Markets; Workers' Perspectives and Africa-Centris AI Platforms. The white paper provides an overview of the current state and trends of generative AI and its applications in different domains, as well as the challenges and risks associated with its adoption and regulation. It represents a diverse set of perspectives to create a set of insights and recommendations which aim to encourage debate and collaborative action towards creating a dignified future of work for everyone across Africa.
First Steps towards K-12 Computer Science Education in Portugal -- Experience Report
Venue: arXiv cs.CY 2024
Year: 2024

Computer scientists Jeannette Wing and Simon Peyton Jones have catalyzed a pivotal discussion on the need to introduce computing in K-12 mandatory education. In Wing's own words, computing 'represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.'' The crux of this educational endeavor lies in its execution. This paper reports on the efforts of the ENSICO association to implement such aims in Portugal. Starting with pilot projects in a few schools in 2020, it is currently working with 4500 students, 35 schools and 100 school teachers. The main aim is to gain enough experience and knowledge to eventually define a comprehensive syllabus for teaching computing as a mandatory subject throughout the basic and secondary levels of the Portuguese educational system. A structured framework for integrating computational thinking into K-12 education is proposed, with a particular emphasis on mathematical modeling and the functional programming paradigm. This approach is chosen for its potential to promote analytical and problem-solving skills of computational thinking aligned with the core background on maths and science.
An Effective Framework to Help Large Language Models Handle Numeric-involved Long-context Tasks
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) have demonstrated remarkable capabilities in handling long texts and have almost perfect performance in traditional retrieval tasks. However, their performance significantly degrades when it comes to numerical calculations in the long-context. Numeric-involved long-context tasks typically cannot be addressed by current LLMs in normal settings due to their inherent limitations in simultaneously handling complex and massive information. Some CoT like prompting methods can improve accuracy but demands massive output tokens, which is costly and slow. To address this issue, we propose a workflow, which decompose a numeric-involved long-context task into 4 low-level subtasks: judging, extracting and processing with code and conclusion. The former 2 subtasks is relatively simple, which allows us to use smaller models for efficiently processing long context. When numerical calculations are required, we use code generated by LLMs to avoid the disadvantage of LLM not being good at calculations. The results in 2 numeric-involved long-context benchmarks demonstrate our workflow can not only improve accuracy, but also significantly reduce the cost of API calls.
Learning Generalizable 3D Manipulation With 10 Demonstrations
Venue: arXiv cs.CV 2024
Year: 2024

Learning robust and generalizable manipulation skills from demonstrations remains a key challenge in robotics, with broad applications in industrial automation and service robotics. While recent imitation learning methods have achieved impressive results, they often require large amounts of demonstration data and struggle to generalize across different spatial variants. In this work, we present a novel framework that learns manipulation skills from as few as 10 demonstrations, yet still generalizes to spatial variants such as different initial object positions and camera viewpoints. Our framework consists of two key modules: Semantic Guided Perception (SGP), which constructs task-focused, spatially aware 3D point cloud representations from RGB-D inputs; and Spatial Generalized Decision (SGD), an efficient diffusion-based decision-making module that generates actions via denoising. To effectively learn generalization ability from limited data, we introduce a critical spatially equivariant training strategy that captures the spatial knowledge embedded in expert demonstrations. We validate our framework through extensive experiments on both simulation benchmarks and real-world robotic systems. Our method demonstrates a 60 percent improvement in success rates over state-of-the-art approaches on a series of challenging tasks, even with substantial variations in object poses and camera viewpoints. This work shows significant potential for advancing efficient, generalizable manipulation skill learning in real-world applications.
Exploring Augmented Table Setup and Lighting Customization in a Simulated Restaurant to Improve the User Experience
Venue: arXiv cs.HC 2024
Year: 2024

This study explored a concept for using Augmented Reality (AR) glasses to customize augmented table setup and lighting in a restaurant. The aim was to provide insights into AR usage in restaurants and contribute to existing research by introducing an extendable and versatile concept for scholars and restaurateurs. A controlled laboratory study, using a within-subjects design, was conducted to investigate the effects of a customizable augmented table setup and lighting on user experience (UX), perceived waiting time, psychological ownership, and social acceptability. A simulated restaurant environment was created using a 360-degree image in Virtual Reality (VR). The study implemented default and customizable table setup and lighting. Results from a paired samples t-test showed a statistically significant effect of table setup and lighting on the pragmatic quality of UX, hedonic quality of UX, overall UX, valence, dominance, psychological ownership, and affect. Furthermore, table setup had a significant effect on arousal and perceived waiting time. Moreover, table setup significantly affected AR interaction, isolation, and safety acceptability, while lighting only affected AR interaction acceptability. Findings suggest that these investigated variables are worth considering for AR applications in a restaurant, especially when offering customizable augmented table setup and lighting.
Generative AI in Multimodal User Interfaces: Trends, Challenges, and Cross-Platform Adaptability
Venue: arXiv cs.HC 2024
Year: 2024

As the boundaries of human computer interaction expand, Generative AI emerges as a key driver in reshaping user interfaces, introducing new possibilities for personalized, multimodal and cross-platform interactions. This integration reflects a growing demand for more adaptive and intuitive user interfaces that can accommodate diverse input types such as text, voice and video, and deliver seamless experiences across devices. This paper explores the integration of generative AI in modern user interfaces, examining historical developments and focusing on multimodal interaction, cross-platform adaptability and dynamic personalization. A central theme is the interface dilemma, which addresses the challenge of designing effective interactions for multimodal large language models, assessing the trade-offs between graphical, voice-based and immersive interfaces. The paper further evaluates lightweight frameworks tailored for mobile platforms, spotlighting the role of mobile hardware in enabling scalable multimodal AI. Technical and ethical challenges, including context retention, privacy concerns and balancing cloud and on-device processing are thoroughly examined. Finally, the paper outlines future directions such as emotionally adaptive interfaces, predictive AI driven user interfaces and real-time collaborative systems, underscoring generative AI's potential to redefine adaptive user-centric interfaces across platforms.
Melanoma Detection with Uncertainty Quantification
Venue: arXiv cs.CV 2024
Year: 2024

Early detection of melanoma is crucial for improving survival rates. Current detection tools often utilize data-driven machine learning methods but often overlook the full integration of multiple datasets. We combine publicly available datasets to enhance data diversity, allowing numerous experiments to train and evaluate various classifiers. We then calibrate them to minimize misdiagnoses by incorporating uncertainty quantification. Our experiments on benchmark datasets show accuracies of up to 93.2% before and 97.8% after applying uncertainty-based rejection, leading to a reduction in misdiagnoses by over 40.5%. Our code and data are publicly available, and a web-based interface for quick melanoma detection of user-supplied images is also provided.
CNN-Based Classification of Persian Miniature Paintings from Five Renowned Schools
Venue: arXiv cs.CV 2024
Year: 2024

This article addresses the gap in computational painting analysis focused on Persian miniature painting, a rich cultural and artistic heritage. It introduces a novel approach using Convolutional Neural Networks (CNN) to classify Persian miniatures from five schools: Herat, Tabriz-e Avval, Shiraz-e Avval, Tabriz-e Dovvom, and Qajar. The method achieves an average accuracy of over 91%. A meticulously curated dataset captures the distinct features of each school, with a patch-based CNN approach classifying image segments independently before merging results for enhanced accuracy. This research contributes significantly to digital art analysis, providing detailed insights into the dataset, CNN architecture, training, and validation processes. It highlights the potential for future advancements in automated art analysis, bridging machine learning, art history, and digital humanities, thereby aiding the preservation and understanding of Persian cultural heritage.
BMP: Bridging the Gap between B-Spline and Movement Primitives
Venue: arXiv cs.RO 2024
Year: 2024

This work introduces B-spline Movement Primitives (BMPs), a new Movement Primitive (MP) variant that leverages B-splines for motion representation. B-splines are a well-known concept in motion planning due to their ability to generate complex, smooth trajectories with only a few control points while satisfying boundary conditions, i.e., passing through a specified desired position with desired velocity. However, current usages of B-splines tend to ignore the higher-order statistics in trajectory distributions, which limits their usage in imitation learning (IL) and reinforcement learning (RL), where modeling trajectory distribution is essential. In contrast, MPs are commonly used in IL and RL for their capacity to capture trajectory likelihoods and correlations. However, MPs are constrained by their abilities to satisfy boundary conditions and usually need extra terms in learning objectives to satisfy velocity constraints. By reformulating B-splines as MPs, represented through basis functions and weight parameters, BMPs combine the strengths of both approaches, allowing B-splines to capture higher-order statistics while retaining their ability to satisfy boundary conditions. Empirical results in IL and RL demonstrate that BMPs broaden the applicability of B-splines in robot learning and offer greater expressiveness compared to existing MP variants.
On the Cost of Model-Serving Frameworks: An Experimental Evaluation
Venue: arXiv cs.LG 2024
Year: 2024

In machine learning (ML), the inference phase is the process of applying pre-trained models to new, unseen data with the objective of making predictions. During the inference phase, end-users interact with ML services to gain insights, recommendations, or actions based on the input data. For this reason, serving strategies are nowadays crucial for deploying and managing models in production environments effectively. These strategies ensure that models are available, scalable, reliable, and performant for real-world applications, such as time series forecasting, image classification, natural language processing, and so on. In this paper, we evaluate the performances of five widely-used model serving frameworks (TensorFlow Serving, TorchServe, MLServer, MLflow, and BentoML) under four different scenarios (malware detection, cryptocoin prices forecasting, image classification, and sentiment analysis). We demonstrate that TensorFlow Serving is able to outperform all the other frameworks in serving deep learning (DL) models. Moreover, we show that DL-specific frameworks (TensorFlow Serving and TorchServe) display significantly lower latencies than the three general-purpose ML frameworks (BentoML, MLFlow, and MLServer).
EHRs Data Harmonization Platform, an easy-to-use shiny app based on recodeflow for harmonizing and deriving clinical features
Venue: arXiv cs.DB 2024
Year: 2024

Electronic health records (EHRs) contain important longitudinal information on individuals who have received medical care. Traditionally, EHRs have been used to support a wide range of administrative activities such as billing and clinical workflow, but, given the depth and breadth of clinical and demographic data they contain, they are increasingly being used to provide real-world data for research. Although EHR data have enormous research potential, the full realization of that potential requires a data management strategy that extracts from large EHR databases, that are collected from a range of care settings and time periods, well-documented research-relevant data that can be used by different researchers. Having a common well-documented data management strategy for EHR will support reproducible research and sharing documentation on research variables that are derived from EHR variables is important to open science. In this short paper, we describe the EHRs Data Harmonization Platform. The platform is based on an easy to use web app a publicly available at https://poxotn-arian-aminoleslami.shinyapps.io/Arian/ and as a standalone software package at https://github.com/ArianAminoleslami/EHRs-Data Harmonization-Platform, that is linked to an existing R library for data harmonization called recodeflow. The platform can be used to extract, document, and harmonize variables from EHR and it can also be used to document and share research variables that have been derived from those EHR data.
Comparative Analysis of Machine Learning Approaches for Bone Age Assessment: A Comprehensive Study on Three Distinct Models
Venue: arXiv cs.CV 2024
Year: 2024

Radiologists and doctors make use of X-ray images of the non-dominant hands of children and infants to assess the possibility of genetic conditions and growth abnormalities. This is done by assessing the difference between the actual extent of growth found using the X-rays and the chronological age of the subject. The assessment was done conventionally using The Greulich Pyle (GP) or Tanner Whitehouse (TW) approach. These approaches require a high level of expertise and may often lead to observer bias. Hence, to automate the process of assessing the X-rays, and to increase its accuracy and efficiency, several machine learning models have been developed. These machine-learning models have several differences in their accuracy and efficiencies, leading to an unclear choice for the suitable model depending on their needs and available resources. Methods: In this study, we have analyzed the 3 most widely used models for the automation of bone age prediction, which are the Xception model, VGG model and CNN model. These models were trained on the preprocessed dataset and the accuracy was measured using the MAE in terms of months for each model. Using this, the comparison between the models was done. Results: The 3 models, Xception, VGG, and CNN models have been tested for accuracy and other relevant factors.
Guaranteed Bounds on Posterior Distributions of Discrete Probabilistic Programs with Loops
Venue: arXiv cs.PL 2024
Year: 2024

We study the problem of bounding the posterior distribution of discrete probabilistic programs with unbounded support, loops, and conditioning. Loops pose the main difficulty in this setting: even if exact Bayesian inference is possible, the state of the art requires user-provided loop invariant templates. By contrast, we aim to find guaranteed bounds, which sandwich the true distribution. They are fully automated, applicable to more programs and provide more provable guarantees than approximate sampling-based inference. Since lower bounds can be obtained by unrolling loops, the main challenge is upper bounds, and we attack it in two ways. The first is called residual mass semantics, which is a flat bound based on the residual probability mass of a loop. The approach is simple, efficient, and has provable guarantees. The main novelty of our work is the second approach, called geometric bound semantics. It operates on a novel family of distributions, called eventually geometric distributions (EGDs), and can bound the distribution of loops with a new form of loop invariants called contraction invariants. The invariant synthesis problem reduces to a system of polynomial inequality constraints, which is a decidable problem with automated solvers. If a solution exists, it yields an exponentially decreasing bound on the whole distribution, and can therefore bound moments and tail asymptotics as well, not just probabilities as in the first approach. Both semantics enjoy desirable theoretical properties. In particular, we prove soundness and convergence, i.e. the bounds converge to the exact posterior as loops are unrolled further. On the practical side, we describe Diabolo, a fully-automated implementation of both semantics, and evaluate them on a variety of benchmarks from the literature, demonstrating their general applicability and the utility of the resulting bounds.
Multiscale Dubuc: A New Similarity Measure for Time Series
Venue: arXiv cs.LG 2024
Year: 2024

Quantifying similarities between time series in a meaningful way remains a challenge in time series analysis, despite many advances in the field. Most real-world solutions still rely on a few popular measures, such as Euclidean Distance (EuD), Longest Common Subsequence (LCSS), and Dynamic Time Warping (DTW). The strengths and weaknesses of these measures have been studied extensively, and incremental improvements have been proposed. In this study, however, we present a different similarity measure that fuses the notion of Dubuc's variation from fractal analysis with the Intersection-over-Union (IoU) measure which is widely used in object recognition (also known as the Jaccard Index). In this proof-of-concept paper, we introduce the Multiscale Dubuc Distance (MDD) measure and prove that it is a metric, possessing desirable properties such as the triangle inequality. We use 95 datasets from the UCR Time Series Classification Archive to compare MDD's performance with EuD, LCSS, and DTW. Our experiments show that MDD's overall success, without any case-specific customization, is comparable to DTW with optimized window sizes per dataset. We also highlight several datasets where MDD's performance improves significantly when its single parameter is customized. This customization serves as a powerful tool for gauging MDD's sensitivity to noise. Lastly, we show that MDD's running time is linear in the length of the time series, which is crucial for real-world applications involving very large datasets.
M-VAR: Decoupled Scale-wise Autoregressive Modeling for High-Quality Image Generation
Venue: arXiv cs.CV 2024
Year: 2024

There exists recent work in computer vision, named VAR, that proposes a new autoregressive paradigm for image generation. Diverging from the vanilla next-token prediction, VAR structurally reformulates the image generation into a coarse to fine next-scale prediction. In this paper, we show that this scale-wise autoregressive framework can be effectively decoupled into \textit{intra-scale modeling}, which captures local spatial dependencies within each scale, and \textit{inter-scale modeling}, which models cross-scale relationships progressively from coarse-to-fine scales. This decoupling structure allows to rebuild VAR in a more computationally efficient manner. Specifically, for intra-scale modeling -- crucial for generating high-fidelity images -- we retain the original bidirectional self-attention design to ensure comprehensive modeling; for inter-scale modeling, which semantically connects different scales but is computationally intensive, we apply linear-complexity mechanisms like Mamba to substantially reduce computational overhead. We term this new framework M-VAR. Extensive experiments demonstrate that our method outperforms existing models in both image quality and generation speed. For example, our 1.5B model, with fewer parameters and faster inference speed, outperforms the largest VAR-d30-2B. Moreover, our largest model M-VAR-d32 impressively registers 1.78 FID on ImageNet 256$\times$256 and outperforms the prior-art autoregressive models LlamaGen/VAR by 0.4/0.19 and popular diffusion models LDM/DiT by 1.82/0.49, respectively. Code is avaiable at \url{https://github.com/OliverRensu/MVAR}.